Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a...

Descripción completa

Detalles Bibliográficos
Autores principales: Abu-Amara, Fadi, Abdel-Qader, Ikhlas
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2009
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766069/
https://www.ncbi.nlm.nih.gov/pubmed/19859576
http://dx.doi.org/10.1155/2009/680508
Descripción
Sumario:We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a feature subset selection module (using rough set model). Rough set model is used to reduce the effect of data inconsistency while a fuzzy classifier is integrated into the system to label subimages into normal or abnormal regions. The experimental results show that this system has an accuracy of 84.03% and a recall percentage of 87.28%.

Ejemplares similares